Abstract In cooperative human decision-making, agreements are often not total; a partial degree of agreement is sufficient to commit to a decision and move on, as long as one is somewhat confident that the involved parties are likely to stand by their commitment in the future, given no drastic unexpected changes. In this paper, we introduce the notion of agreement scenarios that allow artificial autonomous agents to reach such agreements, using formal models of argumentation, in particular abstract argumentation and value-based argumentation. We introduce the notions of degrees of satisfaction and (minimum, mean, and median) agreement, as well as a measure of the impact a value in a value-based argumentation framework has on these notions. We then analyze how degrees of agreement are affected when agreement scenarios are expanded with new information, to shed light on the reliability of partial agreements in dynamic scenarios. An implementation of the introduced concepts is provided as part of an argumentation-based reasoning software library.

We consider a type of information-seeking dialogue between a seeker agent and a respondent agent, where the seeker estimates the respondent to not be willing to share a particular set of sought-after information. Hence, the seeker postpones (hides) its goal topic, related to the respondent's sensitive information, until the respondent is perceived as willing to talk about it. In the intermediate process, the seeker opens other topics to steer the dialogue tactfully towards the goal. Such dialogue strategies, which we refer to as goal-hiding strategies, are common in diverse contexts such as criminal interrogations and medical assessments, involving sensitive topics. Conversely, in malicious online interactions like social media extortion, similar strategies might aim to manipulate individuals into revealing information or agreeing to unfavorable terms. This paper proposes a formal dialogue framework for understanding goal-hiding strategies. The dialogue framework uses Quantitative Bipolar Argumentation Frameworks (QBAFs) to assign willingness scores to topics. An initial willingness for each topic is modified by considering how topics promote (support) or demote (attack) other topics. We introduce a method to identify relations among topics by considering a respondent's shared information. Finally, we introduce a gradual semantics to estimate changes in willingness as new topics are opened. Our formal analysis and empirical evaluation show the system's compliance with privacy-preserving safety properties. A formal understanding of goal-hiding strategies opens up a range of practical applications; For instance, a seeker agent may plan with goal-hiding to enhance privacy in human-agent interactions. Similarly, an observer agent (third-party) may be designed to enhance social media security by detecting goal-hiding strategies employed by users' interlocutors.

Abstract In this paper, we introduce the action language C-MT (Mind Transition Language), built on top of answer set programs and transition systems to represent how human mental states evolve in response to sequences of observable actions. Drawing on well-established psychological theories, such as the Appraisal Theory of Emotion, we formalize mental states, such as emotions, as multi-dimensional configurations. To enable controlled agent behavior and limit undesirable effects such as undue psychological influence, we introduce a novel causal rule, forbids to cause, together with constructs tailored to mental state dynamics. These allow the specification of valid transitions as constraints and invariance properties, which are rigorously evaluated over trajectories in transition systems. The framework supports reasoning about and comparing different dynamics of mental change under varying constraints. We apply the action language to design models for emotion verification.

State-of-the-art intelligent and interactive agents, such as Alexa or Siri, often present overly conforming behaviour during interactions with humans. This can result in a misalignment between end-user expectations and agent behaviour. To overcome this barrier in human-AI interactions, we introduce the Critical Friend (CF), a conceptual idea that guides critical behaviour in human-human interactions. We present our results as a formal understanding that can be described through description logic and utilised for reasoning capabilities, enabling implementations of the CF as an intelligent interactive agent.

Abstract Explainable Artificial Intelligence systems, including intelligent agents, are expected to explain their internal decisions, behaviors and reasoning that produce their choices to the humans (or to other systems) with which they interact. Given this context, the aim of this article is to introduce a practical reasoning agent framework that supports generation of explanations about the goals the agent committed to. Firstly, we present an argumentation-based formalization for supporting goal reasoning. This is based on the belief-based goal processing model proposed by Castelfranchi and Paglieri, which is a more granular and refined model than the Beliefs–Desires–Intentions model. We focus on the dynamics of goals since they are desires until they become intentions, including the conditions under which a goal can be cancelled. We use formal argumentation reasoning to support the passage of the goals from their initial state until their final state. Secondly, in order that agents based on the proposed formalization be able to generate explanations about the goals they decided to commit to, we endow them with a mechanism for generating both complete and partial explanations. Finally, we use a scenario of rescue robots in order to illustrate the performance of our proposal, for which a simulator was developed to support the agents goal reasoning.

Despite the extensive benefits of machine learning techniques in practice, several studies demonstrated that many approaches are vulnerable to attacks. These attacks generate adversarial data to manipulate learning models that result ambiguous decisions. In this paper, we propose a hybrid-reasoning framework that combines data-driven and non-monotonic reasoning, specifically formal argumentation and adversarial regression with multiple learners, to deal with ambiguous predictions of predictive models. The introduced hybrid-reasoning framework ensures three significant benefits. It (i) provides an argumentation-based aggregation function for combining multiple learners, (ii) reduces the effort to resolve conflicts in predictions, and (iii) cost-effective and robust training in adversarial regression solutions. To illustrate the introduced framework, we consider a benchmark of resource traces obtained from Yahoo's service cluster for anomaly detection under adversarial settings. The experimental analysis shows 3% more accuracy in prediction under argumentation-based adversarial settings.

In the symbolic artificial intelligence community, abstract argumentation with its semantics, i.e. approaches for defining sets of valid conclusions (extensions) that can be derived from argumentation graphs, is considered a promising method for non-monotonic reasoning. However, from a sequential perspective, abstract argumentation-based decision-making processes typically do not guarantee an alignment with common formal notions to assess consistency; in particular, abstract argumentation can, in itself, not enforce the satisfaction of relational principles such as reference independence (based on a key principle of microeconomic theory) and cautious monotony. In this paper, we address this issue by introducing different approaches to ensuring reference independence and cautious monotony in sequential argumentation: a reductionist, an expansionist, and an extension-selecting approach. The first two approaches are generically applicable, but may require comprehensive changes to the corresponding argumentation framework. In contrast, the latter approach guarantees that an extension of the corresponding argumentation framework can be selected to satisfy the relational principle by requiring that the used argumentation semantics is weakly reference independent or weakly cautiously monotonous, respectively, and also satisfies some additional straightforward principles. To highlight the relevance of the approach, we illustrate how the extension-selecting approach to reference independent argumentation can be applied to model (boundedly) rational economic decision-making.

Abstract Abstract argumentation has emerged as a method for non-monotonic reasoning that has gained popularity in the symbolic artificial intelligence community. In the literature, the different approaches to abstract argumentation that were refined over the years are typically evaluated from a formal logics perspective; an analysis that is based on models of economically rational decision-making does not exist. In this paper, we work towards addressing this issue by analysing abstract argumentation from the perspective of the rational man paradigm in microeconomic theory. To assess under which conditions abstract argumentation-based decision-making can be considered economically rational, we derive reference independence as a non-monotonic inference property from a formal model of economic rationality and create a new argumentation principle that ensures compliance with this property. We then compare the reference independence principle with other reasoning principles, in particular with cautious monotony and rational monotony. We show that the argumentation semantics as proposed in Dung’s seminal paper, as well as other semantics we evaluate, with the exception of naive semantics and the SCC-recursive CF2 semantics, violate the reference independence principle. Consequently, we investigate how structural properties of argumentation frameworks impact the reference independence principle and identify cyclic expansions (both even and odd cycles) as the root of the problem. Finally, we put reference independence into the context of preference-based argumentation and show that for this argumentation variant, which explicitly models preferences, reference independence cannot be ensured in a straight-forward manner.

In some abstract argumentation framework (AAF), arguments have a degree of uncertainty, which impacts on the degreeof uncertainty of the extensions obtained under a semantics. In these approaches, both the uncertainty of the arguments and of the extensions are modeled by means of precise probability values. However, in many real life situations the exact probabilities values are unknownand sometimes there is a need for aggregating the probability valuesof different sources. In this paper, we tackle the problem of calculat-ing the degree of uncertainty of the extensions considering that theprobability values of the arguments are imprecise

In this paper, we propose a multi-agent framework to deal with situations involving uncertain or inconsistent information located in a distributed environment which cannot be combined into a single knowledge base. To this end, we introduce an inquiry dialogue approach based on a combination of possibilistic logic and a formal argumentation-based theory, where possibilistic logic is used to capture uncertain information, and the argumentation-based approach is used to deal with inconsistent knowledge in a distributed environment. We also modify the framework of earlier work, so that the system is not only easier to implement but also more suitable for educational purposes. The suggested approach is implemented in a clinical decision-support system in the domain of dementia diagnosis. The approach allows the physician to suggest a hypothetical diagnosis in a patient case, which is verified through the dialogue if sufficient patient information is present. If not, the user is informed about the missing information and potential inconsistencies in the information as a way to provide support for continuing medical education. The approach is presented, discussed, and applied to one scenario. The results contribute to the theory and application of inquiry dialogues in situations where the data are uncertain and inconsistent.

Based on an argumentation theory approach, we present a novel method for evaluating complex goal-based activities by generalizing a notion of qualifier defined in the health domain. Three instances of the general qualifier are proposed: Performance, Actuation and Capacity; the first one evaluates what a person does, the second how an individual follows an action plan, and the third one how “well” or “bad” an activity is executed. Qualifiers are intended to be used by autonomous systems for evaluating human activity. We exemplify our approach using a health domain assessment protocol. Main results of this test show a partial correlation between ambiguities assessed by experts and our argument-based approach; and a multi-dimensional perspective how an activity is executed when a combined evaluation of qualifiers is used. This last outcome was interesting for some therapists consulted. Results also show differences between values of qualifiers using different argumentation semantics; two scenarios were proposed by therapist for using different semantics: preliminary activity screening and time-span follow-up evaluation.

Abstract Characterizations of semi-stable and stage extensions in terms of two-valued logical models are presented. To this end, the so-called GL-supported and GL-stage models are defined. These two classes of logical models are logic programming counterparts of the notion of range which is an established concept in argumentation semantics.

We show that the ideal sets of an argumentation framework can be character-ized by two kinds of logical models: ideal models (2-valued logical models) and p-stable models (2-valued logical models). We also show that the maximal ideal set of an argumentation framework can be characterized by the well-founded+ model (a 3-valued logical model). These results argue for the logical foundations of the ideal sets of an argumentation framework. Moreover, these results consoli-date the strong relationship between argumentation semantics and logic program-ming semantics with negation as failure. More accurately, we prove that the five argumentation semantics suggested by Dung et al., grounded, stable, preferred, complete and ideal semantics, can be characterized by the well-founded model, stable-model, p-stable, Clark’s completion and well-founded+ model semantics, respectively by using a unique mapping from argumentation frameworks into logic programs. We observe that the labellings of these argumentation semantics can be inferred by the logical models of a logic program. 1

We introduce the Assisted Living system ALI, which is a novel approach to providing assistance and support in activities of daily life. We integrate a human behavior theory with a default reasoning decision making framework. This integration allows us to model a decision making problem from a human activity centric point of view and at the same time, formalize these elements using a possibilistic argumentation theory. ALI sends personalized notifications suggesting the most suitable activities to perform and determines what activities were performed during a time period.

This paper presents an application of business intelligence (BI) for electricity management systems in the context of the Smart Grid domain. Combining semantic Web technologies (SWT) and elements of grid computing (GC), we have designed a distributed architecture of intelligent nodes, which are called power grid distributed nodes (PGDINs). This distributed architecture supports the majority of the grid management activities in an intelligent and collaborative way by means of distributed processing of semantic data. A node collaborative scheme is defined based on logical states that each node presents according to the events occurring in the grid. A specific BPEL business-workflow is formally defined for each logical state, based on the node's knowledge base (an electrical model) and the distributed data. The introduced core workflows allow the potential grid behavior to be predefined when a business requirement is triggered. Thus, this approach supports the grid to react and reach over again a stable state, which is defined as a working state that facilitates the provision of the required business tasks. We have validated our approach with the simulation of a well-known use case, the energy balancing verification, fed with real data from the Spanish electrical grid.

Abstract In this paper, a possibilistic disjunctive logic programming approach for modeling uncertain, incomplete, and inconsistent information is defined. This approach introduces the use of possibilistic disjunctive clauses, which are able to capture incomplete information and states of a knowledge base at the same time. By considering a possibilistic logic program as a possibilistic logic theory, a construction of a possibilistic logic programming semantic based on answer sets and the proof theory of possibilistic logic is defined. It shows that this possibilistic semantics for disjunctive logic programs can be characterized by a fixed-point operator. It is also shown that the suggested possibilistic semantics can be computed by a resolution algorithm and the consideration of optimal refutations from a possibilistic logic theory. In order to manage inconsistent possibilistic logic programs, a preference criterion between inconsistent possibilistic models is defined. In addition, the approach of cuts for restoring consistency of an inconsistent possibilistic knowledge base is adopted. The approach is illustrated in a medical scenario.

In this paper, we define a class of nested logic programs, called Nested Logic Programs with Ordered Disjunction (LPODs + ), which makes it possible to specify conditional (qualitative) preferences by means of nested preference statements. To this end, we augment the syntax of Logic Programs with Ordered Disjunction (LPODs) to capture more general expressions. We define the LPODs + semantics in a simple way and we extend most of the results of LPODs showing how our approach generalizes the LPODs framework in a proper way. We also show how the LPODs + semantics can be computed in terms of a translation procedure that maps a nested ordered disjunction program (OD + -program) into a disjunctive logic program.

In this paper, we introduce a possibilistic argumentation-based decision making framework which is able to capture uncertain information and exceptions/defaults. In particular, we define the concept of a possibilistic decision making framework which is based on a possibilistic default theory, a set of decisions and a set of prioritized goals. This set of goals captures user preferences related to the achievement of a particular state in a decision making problem. By considering the inference of the possibilistic well-founded semantics, the concept of argument with respect to a decision is defined. This argument captures the feasibility of reaching a goal by applying a decision in a given context. The inference in the argumentation decision making framework is based on basic argumentation semantics. Since some basic argumentation semantics can infer more than one possible scenario of a possibilistic decision making problem, we define some criteria for selecting potential solutions of the problem.

In the literature, there are several approaches which try to perform common sense reasoning. Among them, the approaches which have probably received the most attention the last two decades are the approaches based on logic programming semantics with

Extension-based argumentation semantics have shown to be a suitable approach for performing practical reasoning. Since extension-based argumentation semantics were formalized in terms of relationships between atomic arguments, it has been shown that extension-based argumentation semantics based on admissible sets such as stable semantics can be characterized in terms of answer sets. In this paper, we present an approach for characterizing SCC-recursive semantics in terms of answer set models. In particular, we will show a characterization of CF2 in terms of answer set models. This result suggests that not only extension-based argumentation semantics based on admissible sets can be characterized in terms of answer sets; but also extension-based argumentation semantics based on Strongly Connected Components can be characterized in terms of answer sets.

Abstract Given an argumentation framework AF, we introduce a mapping function that constructs a disjunctive logic program P, such that the preferred extensions of AF correspond to the stable models of P, after intersecting each stable model with the relevant atoms. The given mapping function is of polynomial size w.r.t. AF. In particular, we identify that there is a direct relationship between the minimal models of a propositional formula and the preferred extensions of an argumentation framework by working on representing the defeated arguments. Then we show how to infer the preferred extensions of an argumentation framework by using UNSAT algorithms and disjunctive stable model solvers. The relevance of this result is that we define a direct relationship between one of the most satisfactory argumentation semantics and one of the most successful approach of nonmonotonic reasoning i.e., logic programming with the stable model semantics.